Fetal Health Classification based on CTG using Machine learning

Computer science and engineering

The UN has estimated that 24 million babies were born in India in 2017 and 35,000 mothers died during or shortly after birth, with MMR at 145 per 100,000 live births, or 12% of parental deaths worldwide. Classification of fetal health is an important aspect of child care and can help prevent negative consequences. Cardiotocography (CTG) is a method often used to monitor fetal health, but its interpretation can be subjective and inaccurate. In recent years, machine learning methodologies and techniques have been proposed as a solution for improvement in the accuracy of CTG-based classification of fetus. In this study, we developed a classifier that automatically predicts fetal health using different learning machines. We used a database of CTG data from the University of California, Irvine's Machine Learning Repository containing 2126 subjects and 21 features, including 1655 healthy subjects, 295 unhealthy subjects and pathologically diseased there are 176 subjects. The proposed model has the potential to improve the accuracy and purpose of CTG-based fetal health classification, leading to better prenatal care and better outcomes for mothers and foetuses. As a comparison, the best model for prediction is random forest with 96% accuracy.

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